Advanced Analytics & Tools Training

Your own pace, Your own Time

Module Design

The objective of this training is to introduce the student to fundamentals of Statistical analysis on Business Data. Module 1 starts with the assumption that the student does not have any background in formal statistics or mathematics. Fundamentals are built from ground up.

Modules 2 and 3 cover advanced topics such as Predictive modelling and subjective segmentation. Students will need to have familiarity with topics covered in Module 1 to adequately follow the training.

Module 4 covers advanced data mining and machine learning algorithms in Business Analytics. It is covered exclusively in R and Statistica. (Prior knowledge of R programming is not required).

All modules follow a instruction-case study implementation approach. Students will be using datasets and implementing the techniques in the classroom along with the trainers. Additional datasets for practice will be provided.

Analytics Training

Our Module based approach to Analytics methodologies ensures that we cater to all kinds of students, from freshers or beginners with no background to experienced professionals looking for trainings in advanced data modelling and machine learning.

We have divided our modules numbering 1 through 4, covering a natural progression of topics. Though a student can enrol for any module, prior knowledge and experience of topics covered in earlier modules would be necessary to follow the training.

Module 1 - Basic Analytics

Hours

Statistics Basics

Introduction to Data Analytics and Statistical Techniques

8

Types of Variables, measures of central tendency and dispersion

Variable Distributions and Probability Distributions

Normal Distribution and Properties

Central Limit Theorem and Application

Hypothesis Testing

Null/Alternative Hypothesis formulation

8

One Sample, two sample (Paired and Independent) T/Z Test

P Value Interpretation

Analysis of Variance (ANOVA)

Chi Square Test

Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)

Multivariate Regression

Introduction to Correlation - Karl Pearson and Graphical Methods

8

Spearman Rank Correlation

OLS Regression - Simple and Multiple

Module 2 - Advanced Analytics

Hours

Logistic Regression

Non Linear Regressions using Link functions

8

Logit Link Function

Binomial Propensity Modeling

Training-Validation approach

ROC-AUC, Lift charts, Decile Analysis

Factor Analysis

Introduction to Factor Analysis - PCA

4

KMO MSA tests, Eigen Value Interpretation

Factor Rotation and Extraction

Cluster Analysis

Introduction to Cluster Techniques

4

Distance Methodologies

Hierarchical and Non-Hierarchical Procedures

K-Means clustering

Wards Method

Module 3 - Time Series Analysis

Hours

Introduction and Exponential Smoothening

Introduction to Time Series Data and Analysis

8

Decomposition of Time Series

Trend and Seasonality detection and forecasting

Exponential Smoothing (Single, double and triple)

ARIMA Modeling

Box - Jenkins Methodology

8

Introduction to Auto Regression and Moving Averages, ACF, PACF

Detecting order of ARIMA processes

Seasonal ARIMA Models (P,D,Q)(p,d,q)

Introduction to Multivariate ARIMA

Module 4 - Advanced Data Mining

Hours

Introduction to R/Rattle Environment

R-Rattle GUI Familiarization

2

Rattle Tabs

Data Import and Variable role setting

Data Exploration and Visualization, Hypothesis Testing

Data Manipulation, Standardization, Missing value Treatment

Statistical Analysis & Data Mining/Machine Learning

Cluster Analysis using R-Rattle

6

Association Rule Mining

Predictive Modeling using

Decision Trees

Random Forests

Adaptive Boosting

Logistic Regression

Evaluating & Deploying Models

Evaluating performance of Model on Training and Validation data

2

ROC, Sensitivity, Specificity, Lift charts, Error Matrix

Deploying models using Score options

Opening and Saving models using Rattle

We provide trainings for Module 1 through 3 on a variety of platforms using SAS, SPSS, Statistica, R/Rattle. However Module 4 is covered exclusively on R(Rattle) or Statistica.